For complex digital circuits, building their power models is a popular approach to
estimate their power consumption without detailed circuit information. In the literature,
most of power models have to increase their complexity in order to meet the accuracy
requirement. In this paper, we propose a tableless power model for complex circuits that
uses neural networks to learn the relationship between power dissipation and input/
output signal statistics. The complexity of our neural power model has almost no relationship
with circuit size and number of inputs and outputs such that this power model
can be kept very small even for complex circuits. Using such a simple structure, the
neural power models can still have high accuracy because they can automatically consider
the non-linear characteristic of power distributions and the effects of both statedependent
leakage power and transition-dependent switching power. The experimental
results have shown the accuracy and efficiency of our approach on benchmark circuits
and one practical design for different test sequences with wide range of input distributions.

Received November 8, 2004; revised January 19, 2005; accepted March 23, 2005.
Communicated by Chung-Yu Wu.
*This work was supported in part by the National Science Council of Taiwan, R.O.C., under contract No. NSC
93-2215-E-008-031.